ARTIFICIAL INTELLIGENCE IN BLOCKCHAIN-PROVIDE DIGITAL TECHNOLOGY

Artificial intelligence technologies, today, are rapidly developing and are an important branch of Computer Science. Artificial intelligence is at the heart of research and development of theory, methods, technologies, and applications for modeling and expanding human intelligence. Artificial intelligence technology has three key aspects, namely data, algorithm, and computing power, in the sense that training an algorithm to produce a classification model requires significant data, and the learning process requires improved computing capabilities. In the age of big data, information can come from a variety of sources (such as sensor systems, Internet of Things (IoT) devices and systems, as well as social media platforms) and/or belong to different stakeholders. This mostly leads to a number of problems. One of the key problems is isolated data Islands, where data from a single source/stakeholder is not available to other parties or training an artificial intelligence model, or it is financially difficult or impractical to collect a large amount of distributed data for Centralized Processing and training. There is also a risk of becoming a single point of failure in centralized architectures, which can lead to data intrusion. In addition, data from different sources may be unstructured and differ in quality, and it may also be difficult to determine the source and validity of the data. There is also a risk of invalid or malicious data. All these restrictions may affect the accuracy of the forecast. In practice, artificial intelligence models are created, trained, and used by various subjects. The learning process is not transparent to users, and users may not fully trust the model they are using. In addition, as artificial intelligence algorithms become more complex, it is difficult for people to understand how the result of training is obtained. So, recently there has been a tendency to move away from centralized approaches to artificial intelligence to decentralized ones

Open Data

Open data is freely usable, reusable, or redistributable by anybody, provided there are safeguards in place that protect the data’s integrity and transparency. This book describes how data retrieved from public open data repositories can improve the learning qualities of digital networking, particularly performance and reliability. Chapters address such topics as knowledge extraction, Open Government Data (OGD), public dashboards, intrusion detection, and artificial intelligence in healthcare

Anomaly detection for IoT networks using machine learning

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe Internet of Things (IoT) is considered one of the trending technologies today. IoT affects various industries, including logistics tracking, healthcare, automotive and smart cities. A rising number of cyber-attacks and breaches are rapidly targeting networks equipped with IoT devices. This thesis aims to improve security in IoT networks by enhancing anomaly detection using machine learning. This thesis identified the challenges and gaps related to securing the Internet of Things networks. The challenges are network size, the number of devices, the human factor, and the complexity of IoT networks. The gaps identified include the lack of research on signature-based intrusion detection systems used for anomaly detection, in addition to the lack of modelling input parameters required for anomaly detection in IoT networks. Furthermore, there is a lack of comparison of the performance of machine learning algorithms on standard and real IoT datasets. This thesis creates a dataset to test the anomaly binary classification performance of the Neural Networks, Gaussian Naive Bayes, Support Vector Machine, and Decision Trees machine learning algorithms and compares their results with the KDDCUP99 dataset. The results show that Support Vector Machine and Gaussian Naive Bayes perform lower than the other models on the created IoT dataset. This thesis reduces the number of features required by machine learning algorithms for anomaly detection in the IoT networks to five features only, which resulted in reduced execution time by an average of 58%. This thesis tests CNNwGFC, which is an enhanced Convolutional Neural Network model, in detecting and classifying anomalies in IoT networks. This model achieves an increase of 15.34% in the accuracy for IoT anomaly classification in the UNSW-NB15 compared to the classic Convolutional Neural Network. The CNNwGFC multi-classification accuracy (96.24%) is higher by 7.16 than the highest from the literature

Análisis bibliométrico de información en salud basado en PubMed disponible en las redes sociales: un estudio de La India

Social networks have long been used to disseminate health-related information and help, and this use has increased with the emergence of online social media. The goal of this study is to conduct a bibliometric analysis of health information in the context of India. The literature available in PubMed is the source of the study. The objective of this paper is to develop a better insight into the literature on social media-based health information using bibliometric analysis in the context of India. The software used for bibliometric analysis is profile research networking software from Harvard University and Vosviewer. From the study, it is clear that social media is important in the context of public health. We also found out that although the number of publications in journals is highest but video-audio content has been cited more. Although there is a significant increase in publication during 2020, but number of researchers are still very few. It is clear that social media is of greater importance for marginalized people; health care providers and regulators must take precautions to avoid possible negative outcomes.Las redes sociales se han utilizado durante mucho tiempo para difundir información y ayuda relacionadas con la salud, y este uso ha aumentado con la aparición de las redes sociales en línea. El objetivo de este estudio es realizar un análisis bibliométrico de la información sanitaria en el contexto de la India. La literatura disponible en PubMed es la fuente del estudio. El objetivo de este artículo es desarrollar una mejor comprensión de la literatura sobre la información de salud basada en las redes sociales utilizando el análisis bibliométrico en el contexto de la India. El software utilizado para el análisis bibliométrico es un software de redes de investigación de perfiles de la Universidad de Harvard y Vosviewer. Del estudio, queda claro que las redes sociales son importantes en el contexto de la salud pública. También descubrimos que aunque el número de publicaciones en revistas es mayor, se ha citado más contenido de video-audio. Aunque hay un aumento significativo de la publicación durante 2020, el número de investigadores sigue siendo muy reducido. Está claro que las redes sociales son de mayor importancia para las personas marginadas. Los proveedores de atención médica y los reguladores deben tomar precauciones para evitar posibles resultados negativos

Information Security and Cryptography-Encryption in Journalism

The purpose of this review paper is to garner knowledge about the information security and cryptography encryption practices implementation for journalistic work and its effectiveness in thwarting software security breaches in the wake of ‘Journalism After Snowden’. Systematic literature review for the ‘information security and cryptography encryption in journalism’ employed with an eye to synthesize existing practices in this field. For this, at first the existing approachable research article databases and search engines employed to download or get the abstract of relevant scientific articles which are then used for citation and summarization works in a systematic rigorous anatomization. Contingent upon them their analysis and synthesis employed to arrive at the findings. Research papers collated for the purpose of writing this review paper lighted up the vital issues related to investigative journalists’ safety practices promulgation inadequacies even after the UNESCO 2017 and 2022 guidelines for urgent instrumentalization needs of journalists on the part of its’ member States.Lattice Science Publication (LSP) © Copyright: All rights reserved

Attention and Social Cognition in Virtual Reality:The effect of engagement mode and character eye-gaze

Technical developments in virtual humans are manifest in modern character design. Specifically, eye gaze offers a significant aspect of such design. There is need to consider the contribution of participant control of engagement. In the current study, we manipulated participants’ engagement with an interactive virtual reality narrative called Coffee without Words. Participants sat over coffee opposite a character in a virtual café, where they waited for their bus to be repaired. We manipulated character eye-contact with the participant. For half the participants in each condition, the character made no eye-contact for the duration of the story. For the other half, the character responded to participant eye-gaze by making and holding eye contact in return. To explore how participant engagement interacted with this manipulation, half the participants in each condition were instructed to appraise their experience as an artefact (i.e., drawing attention to technical features), while the other half were introduced to the fictional character, the narrative, and the setting as though they were real. This study allowed us to explore the contributions of character features (interactivity through eye-gaze) and cognition (attention/engagement) to the participants’ perception of realism, feelings of presence, time duration, and the extent to which they engaged with the character and represented their mental states (Theory of Mind). Importantly it does so using a highly controlled yet ecologically valid virtual experience